First, I strongly prefer using JAGS instead of BUGS. JAGS is more stable and offers a little more flexibility. The model specifications language is nearly identical. Search the blog for various posts about converting from BUGS to JAGS.

Now, on to your question: You can define likelihood functions that are not built into JAGS by using the "Bernoulli ones trick" or the "Poisson zeros trick". Search the web for examples, When trying it out yourself, start with simple examples to test out the idea. Then build up to your real application.

Other readers should feel free to reply with more detailed suggestions!

Let us know how it goes.

On Mon, Jul 1, 2013 at 9:21 AM, Gwendolyn Campbell [via Doing Bayesian Data Analysis]

<[hidden email]> wrote:

Hi,

My colleagues and I have data from an experiment (3 levels of IV; approximately 14-20 participants per condition). We think the data come from one or more (hopefully more!) skewed populations. I have downloaded the 'sn' package into the R library folder - now I'm trying to modify your code from ANOVAonewayNonhomogvarBrugs.R to use dsn instead of dnorm. Is this possible? Is it reasonable? We're new to the "Bayesian way" and I would greatly appreciate any help or advice. Thanks so much! :)

--Gwen

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